Method and Apparatus for Live-Object Detection

ABSTRACT

An FMCW radar is used to detect live objects by processing the matched, filtered radar return on a frame by frame basis. An FFT cross correlation coefficient is computed, followed by computing a modified geometric mean of the absolute value of the cross correlation coefficients. The modified geometric mean is then compared to a preset threshold to determine whether the object is moving or is stationary.

CLAIM OF PRIORITY

This application claims priority under 35 U.S.C. 119(e) (1) to U.S. Provisional Application No. 62/142,086 filed Apr. 2, 2015.

TECHNICAL FIELD OF THE INVENTION

The technical field of this invention is radar based live object detection.

BACKGROUND OF THE INVENTION

Recent years have witnessed widespread use of millimeter wave (mm-Wave) radars for advanced driver assistance system (ADAS) applications. Compared with other sensing modalities such as a camera, radar has the ability to perform equally well during different times of the day and can be deployed out of sight behind the car bumper or the doors. In many ADAS applications such as parking, cruise control, and braking, the radar is primarily used to find the three-dimensional location of objects around the vehicle. This includes range, azimuth angle, and elevation angle. The range is computed from the round trip delay of the transmitted signal and the two-dimensional (2D) angle is estimated by using the data collected by an antenna array employing a beamforming-based or an eigen-decomposition based high-resolution frequency estimation method.

The use of radar sensors in automotive pedestrian recognition systems is of special interest since radar sensors are less influenced by environmental conditions (e.g. fog, rain, etc.) as other systems like video cameras. Moreover, high resolution radar sensors are available in many modern vehicles as a part of Adaptive Cruise Control (ACC) systems.

SUMMARY OF THE INVENTION

A 77 GHz radar is used to detect a moving object in its view. This invention is based on processing the matched-filtered radar return on a frame-by-frame basis. For each window, the zeroth lag FFT cross-correlation coefficient is computed of the first chirp in the first frame with the first chirp in subsequent frames. Modified geometric mean (MGM) of the absolute values of these cross-correlation coefficients is then computed and compared with a threshold. This MGM serves as a decision measure to distinguish between a static scene and a scene that has a living object present in it.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of this invention are illustrated in the drawings, in which:

FIG. 1 is a block diagram of the radar employed in the invention;

FIG. 2 shows the received radar frames;

FIG. 3 is a block diagram of the method of this invention; and

FIG. 4 is a plot of experimental results.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As shown in FIG. 1, a radar based pedestrian recognition system may consist of an RF transmitter and receiver (102), a transmit/receive antenna (103) and a signal processing unit (101). The implementation shown employs a 77 GHz FMCW (frequency modulated continuous wave) radar. FIG. 2 shows the received radar frames after they are matched-filtered with the transmitted chirps. Each frame contains multiple FMCW chirps, where the matched filtered chirps are called the beat signal x(n). For a frame, only the first chirp will be considered in the processing shown in this invention.

The live object detection method of this invention is shown in FIG. 3.

-   (301) The beat signal corresponding to the first received chirp of     each frame is represented by x(n,k), where k is the frame index and     n is the time index. -   (302) Compute fast Fourier transform (FFT) of x(n,k). Call it     X(f,k), a column vector of size K, where K is FFT size. -   (303) For the ith window of size W frames chosen by the user,     compute the zeroth-lag cross correlation of the FFT of first frame     with all subsequent frames in the window. Call this     cross-correlation C₀(i,k). It is computed as:

${{C_{0}\left( {i,k} \right)} = {\frac{1}{{{X^{H}\left( {f,0} \right)}{X\left( {f,0} \right)}{{{X^{H}\left( {f,k} \right)}{X\left( {f,k} \right)}}}}}{X^{H}\left( {f,0} \right)}{X\left( {f,k} \right)}}},{k = 0},\ldots \mspace{14mu},{W - 1.}$

(304) Compute the modified geometric mean of W cross coefficients as:

${G(i)} = {\prod\limits_{k = 0}^{W}\; {{{C_{0}\left( {i,k} \right)}}.}}$

(305) Declare the processing frame W of ith window as containing a live object or being static based on the following criterion:

If G(i)≧γ, the scene is declared static

If G(i)<γ, the scene is declared to have a live object present in it.

The threshold γ ranges between 0 and 1 and is chosen by the user beforehand.

FIG. 4 shows a plot of γ with values between 0 and one, plotted against 200 frames, showing live objects 401 and static objects 402. 

What is claimed is:
 1. A method of moving object detection comprising the steps of: generating a frequency modulated continuous wave (FMCW) signal; transmitting said signal in the direction of the object to be detected; receiving the reflected signal from said object; forming a plurality of beat signal chirps by mixing the received signal with the transmitted signal; forming a plurality of frames containing a predetermined number of chirps; forming a plurality of windows containing a predetermined number of frames; computing fast a Fourier transform (FFT) X(f,k) of x(n,k) where k is the frame index and n is the time index within a frame; computing the zeroth-lag cross correlation C₀(i,k) of the FFT of the first frame with all subsequent frames in the window; computing the modified geometric mean of the cross coefficients; comparing said geometric mean with a predetermined threshold to determine if said object is static or moving.
 2. The method of claim 1, wherein: said zeroth-lag cross correlation is computed by ${{C_{0}\left( {i,k} \right)} = {\frac{1}{{{X^{H}\left( {f,0} \right)}{X\left( {f,0} \right)}{{{X^{H}\left( {f,k} \right)}{X\left( {f,k} \right)}}}}}{X^{H}\left( {f,0} \right)}{X\left( {f,k} \right)}}},{k = 0},\ldots \mspace{14mu},{W - 1}$
 3. The method of claim 1, wherein: said modified geometric mean G(i)is computed by: ${G(i)} = {\prod\limits_{k = 0}^{W}\; {{{C_{0}\left( {i,k} \right)}}.}}$
 4. An apparatus for moving object detection comprising of: a radio frequency transmitter operable to generate a frequency modulated continuous wave signal; an antenna operable to transmit and receive said signal; a processor operable to perform the steps of: forming a plurality of beat signal chirps by mixing the received signal with the transmitted signal; forming a plurality of frames containing a predetermined number of chirps; forming a plurality of windows containing a predetermined number of frames; computing fast a Fourier transform (FFT) X(f,k) of x(n,k) where k is the frame index and n is the time index within a frame; computing the zeroth-lag cross correlation C₀(i,k) of the FFT of the first frame with all subsequent frames in the window; computing the modified geometric mean of the cross coefficients; comparing said geometric mean with a predetermined threshold to determine if said object is static or moving.
 5. The apparatus of claim 4, wherein said processor is further operable to compute said zeroth-lag cross correlation by: ${{C_{0}\left( {i,k} \right)} = {\frac{1}{{{X^{H}\left( {f,0} \right)}{X\left( {f,0} \right)}{{{X^{H}\left( {f,k} \right)}{X\left( {f,k} \right)}}}}}{X^{H}\left( {f,0} \right)}{X\left( {f,k} \right)}}},{k = 0},\ldots \mspace{14mu},{W - 1}$
 6. The apparatus of claim 4, wherein said processor is further operable to compute said modified geometric mean G(i) by: ${G(i)} = {\prod\limits_{k = 0}^{W}\; {{C_{0}\left( {i,k} \right)}}}$ 